How Health Care Systems Shape End-of-Life Care—A Step Toward Transparency
Bibliographic record
Abstract
Our health care systems, in all their multifaceted complexities, are more influential in shaping the delivery of care than individual human effort or error.1,2 Influential system-level factors span many different domains: how we are paid, the buildings we work in, the technology around us, who and how many we have on the team caring for patients, our workload, and our local social networks of influence.Our understanding of these factors and how they shape patient care and outcomes remains rudimentary despite widespread acceptance of their importance.This knowledge gap around system factors is especially large in the field of serious illness and end-of-life care, where the longstanding focus has been on studying and improving individual-level communication between clinicians, patients, and their families.This focus is well justified and should continue.The goal of better end-of-life care is about better decisions, and it seems a reasonable hypothesis that this can be accomplished through better human interactions between those making these hard decisions.Yet, evidence suggests that even high-quality, point-of-care communication among patients, surrogates, and families often fails to overcome the underlying and underexplored system factors that drive care for patients with serious illness.3 Why are system-level factors so difficult to study and modify?Consider the parable from author David Foster Wallace's 2005 graduation address at Kenyon College.4 Two young fish meet an older
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.066 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.038 |
| Scholarly communication | 0.021 | 0.035 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.010 | 0.023 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".